Docking rigid macrocycles using Convex-PL, AutoDock Vina, and RDKit in the D3R Grand Challenge 4

被引:0
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作者
Maria Kadukova
Vladimir Chupin
Sergei Grudinin
机构
[1] Univ. Grenoble Alpes,
[2] CNRS,undefined
[3] Inria,undefined
[4] Grenoble INP,undefined
[5] LJK,undefined
[6] Moscow Institute of Physics and Technology,undefined
关键词
Protein–ligand docking; Ensemble docking; Macrocycle modeling; Convex-PL; Conformer generation; D3R; Drug Design Data Resource; Scoring function;
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学科分类号
摘要
The D3R Grand Challenge 4 provided a brilliant opportunity to test macrocyclic docking protocols on a diverse high-quality experimental data. We participated in both pose and affinity prediction exercises. Overall, we aimed to use an automated structure-based docking pipeline built around a set of tools developed in our team. This exercise again demonstrated a crucial importance of the correct local ligand geometry for the overall success of docking. Starting from the second part of the pose prediction stage, we developed a stable pipeline for sampling macrocycle conformers. This resulted in the subangstrom average precision of our pose predictions. In the affinity prediction exercise we obtained average results. However, we could improve these when using docking poses submitted by the best predictors. Our docking tools including the Convex-PL scoring function are available at https://team.inria.fr/nano-d/software/.
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页码:191 / 200
页数:9
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